{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print 'Training data shape: ', X_train.shape\n",
    "print 'Training labels shape: ', y_train.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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hmSMvUSyOAvAHv/t/vyyNG+t1E0Wd8Tfk81rGMJP16Hwex3HKE3FcfF/72Hcl5aF/+elP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ZXhQO8gtDDzGz+YzVbNgMFp+t+jDsIyaypV4MBgIA4c35Ccr6tSelK+WXRYnpjDcrEOixgOT2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ObO9nbXpsha7Qkf1HDUt0hGQNt+y/PMzs1ms2lTfp1Ox0oFucqH4jRVTVVxcoX6R7sawp0u\nEhUXyBkzKVFQWkO2C7TZLNIH9Hx6N7IcOXGc4n2Whvm9f+/fRZUBGpvb23jjDUrzDfp9dDgqM+K5\nsdnt4sQJGt9+OMTiIkUoBoOBjexde/Eyci44jvMc2+sU0ajDg2vod9LcIJ8yuC9g8PABRZnGYR8x\no+peungGiwzIee2113DrPUINDrtD+Px+v9uzReO///u/b7MUreY89rfXARDp4vs3bgEAzp49hyf7\n9P0Vz8U3vv5lev/0KuKEzo80ywFuh+N7k2hru4mEeYqSJEGeTR+JalX6WJunNgaqj/GYnn+Qn0Uc\n0ljuDzNU29TfjeYQn3mZAA0rSwPceofOAFNJcfmzND4fvPce9nfpDJPBS3BXmPMqk1Ah6XBWVQ3w\nGFzkbWPrIZ0TS7UWkoj65OMPh/iNX6dz8dzFNUTxAX9PhvmFmanb+PqXXselq/Rs3//TEbpcLlP3\nfQjQhp1riayYGEIi5aiw8FzECX0+SV1wpQqunzMYcWnAtRcG+Bcf0P7UGQMZS8zkSYTZs4RKTVQX\n8S7JA2VJhGjMiLyNdcwyeCXRAqNtGutMC9S8TxaJ+tQvUXNzTRIKBOAZDzHDiXO3YekFNg5CmAcU\n7p9pttGoso5X4MHhtIHvu7aGRxiJyy9RSPKtd97BzvvEuprkGo8e0gK+cKmBmRZNQC1C3H/IbMII\nkae0aGYaPnLugsFR34bslSMmRJ1T2OxcBSd4sp974Tq++qvEXv2ZV69bbR9ggmoymBAUCyGOXTqO\nacCJyTOMwwiHh4S4icIBqqxf9Y1f/Trm+HfHcYq79yh8u7zUhtE0SY/CEbZu0WI9f76Jc2FxKc0n\nzMvPuNt5HLJuVAPMt2kRuY6LLl/OkjwngWVuj7ZUBrm9vPrSQ84pzN7uQ7TmiDZg1lvAScXw24wW\n6y5G2OXUw/ZohJAXjQTX1YF0mRIOx6ZaIOEUYmJypMxQ63yCMVxuL2OhRn35cLiOc2eopmxzZ8vS\nJ4jmAu7s0GXg1u0b2OON/s7tm5hh4s21tTWAkR5uZrC6St9z6uQZi/jU2iAuNKWUgl9oOWo5oeiQ\nAh7XajQaVXQYSbf+cAtVn94/fWbRMpBPYzlSxNyXvudjHFIfP9nasumzIAhw+hRtvpcuXcKFC1S3\nuLi0hAqT7n3rW9/CqdOn7WeKZ4iiCP/7P/yHAICfvPUTXLlCtXsvv/wK9ndo/b35ve+iUaNx29h4\niEVm3x+FkSUuDCpVVHxO7yuJKbPOAIAwz1DlWgpHAzUW+TWZwrhH4xKN+0VWFtWVCppMH5JFCTbu\n0lwMsyHCZGzbVdR6ffbVz6EI4Hc6Hdy+TdB+Isc9DqVmxG2eMiQYkCqHZmHrdOhjdYH61lTGEO50\naWfFcyWXEwcldbTNKQjfg/MirVFdG8JssjJCKtBj0e//+w/+GN/8JrFWb23vYuMJ7ZkvXr4EXagb\nFDQXD+/DFOkNB4g1vZ5rBTCMYK3WFHp8OO3uH2DIDtX5K1fx/ofUP1tbOxCt6TQQe/0++oxQkyLD\n1gaLrx/u4qUzdAFoKI2U0cgnz5zGiy+SQ/NHf/iHOHWaPvPS9etwWEtuFI6xyIzt7SdPcNghgtGr\nn/k8tpmE8/ade1hlPbtGw0fbkmcK5FlRv+bBYwqbXOd2veZ5jk/gdyNOeugc0f45zBz0Y5qnfQFb\nMpA0U2TgdHGvi/s/pTPyxo9/ivWH1D97gxTvfPgdAMDKbAWtCu1DO08+QkdRqu7m+2/i0hlaE7e2\nurhxk86JE6cC7HGtJbo5jg7pt5bbq2jwseVVM2hOp7YbFfjB9Izl/dEhDo9o7kf9I0h2UFJ3DrlH\nFxjjawhOxTrOpE6z2mhZPd26lGDhBESygxfP0Wdevwr8+79F3/nxA408oXm/tiBRrdK8W8+H+MtH\nXDccGQQFzZBOkfRpLgdBFeB6PUcaXF6Yvo1Amc4rrbTSSiuttNJKey779HmiFPE6AYCSAWBYYbtS\nR42LEaM8xY1HdBPMkg6qirklKi5qrOzeqFfBQD1UXAXNKJW9/T4cl26OuXZw8x6lIn7ha99Ag2Ut\nWr0RbtyjQuUffLCO5Xm6BbdrPlLugih3bZF2lmnL2zGNvfGl6zjJPBxf+PqvYXGFPADyoAv/RPwr\n5VMKM2ISoUry3KJfOoddJIwCEiaD4vSS8R30I0p1xIMBMiYzu/X4Dg6PyKO+e3cDWw+o7f/hhd+G\nYM9JZyEwLQqBIzpRlqHDHDAGBuGxIu+JXp6BLPigIOEwiaArHURMqhnqTQQ5FefOJwazkonmmNNl\nrIFDTln1dGJ5p2p+BZq5s+Kn9O2kTTm6AqgwwsN3nKnTeVnmYWWZJFqW5/fRqnDEbbkKw0Wfdx8/\nwMwMzUfppOiybJHvO6hxNKPdnodbZSSXEKgz8lApFz4X7WZ5atOzSZxCOex1ey6kIK8vzVz0OtQH\nR4djG9moVAUkj5vrKKstOY05joPRiMZgPB5jf3eP257Z1NXi4hJe//znAQCnTp3CEhd1V2o1RiMB\nT548wZkzZ6i9s7OW2K4SVPH5LxIibzQOceUypVJe+8zLuH+XPMP33nvbFq6HUYh9JudsNFo2BSal\nsB6p4yibTp3G5k7PovAjK4bS6AAQDSMoRu21mj7qi9TPC6dmsLywzJ8Z44MPKeW1tTWyETatNS5d\novTGa597Dbs79Myrq6tWT7DX68FjosrDw0MblQKM9bTjeAR3zPxRj0YYDOn9mWUfxpkOuVasXyEF\nJHNR5UojN0UkWEO26DmcS/MwDUYDbveBHr3+3rf/EktLNL9feeUa1u9TRHVlcQ6rqxTFOWKyzXpQ\nRZEDmG23cLRNheoOJOYXCaDj+T629iglf9gdoNamqN1Rf4RBMc91CuNMN1eDILAyR6NBH+mIQR6O\nwPoD2t9PL7RsIf/qyTXMFPJaeYZTZ8/QcwWVCfGu4yApZIUyg4gRsU+2t/HlL1MK78bHH+PHP/o+\nAODUqd+yRfx5bqAEzc00TZHwnjIcDq22pH8sEjmNjTALE9HfKtSRcXo8dTziSwKw3D7AWpv2xs1h\nghUm5RWuxuoZGr+bDzOwmg/2tvfxC6/T+tvuZsiHtL5PLTQRjymKlWQdDPcoytheegPdMaX5atpD\nkFLU/MSZk3jpGs0DeAlSjpIlKoZmrrVp7M/+8i18+3vMhXb7CDOSxusn212bBq0GCjMcLXbzEVoN\n2utXDwdYW6RxD7MAjRqt3bdvKSRcZP6Nzxn8279G0bPuUYiDPRqXtVWFGssjbd3v4nyd+aBMjgYD\nMBxH2EyFIwfwC5SzNpDm/2fpPOgck+1EQjiFaK2x6Q3lVuEF7eIPIJhcMc5ihMwy3I3G9tB0lWPT\nXueufwlVThWMwxF8Lmba2O0h6PEhNIzRXqDcrD9XgcNoQZNnxH8AQGoJhxFTELl9fxqbW2ihy5pY\nd+7fwxGL/7aaNbsRu64Lh8nASFCxED7WNhXWGw6eSgkU7M8mE9i4TxO/7iuwbBKO9vcwOqJFtrv+\nEEPWPAuMC4cRaoONAyRFOqrVhGThW52nltDuWWZEcdHUiDntQxB5Tk/qpy9jVltKKHicBw/jBGO+\nAPlKocbpuvF2F3cSeu5NphI4ilLscU3GCCkyHoscxmoxxVmKrKBSkBIOf8YVgMO/H7iurWV5lm0d\n9pAzBcPV175MGoAAKoGL/X2aXx/euwWX2bDPn1vGxROMiulG2NqkNlSUj7kWHdD1iguPiShH48iy\n8Rrkk4KWYzQQSTaCW+jQjQ02Nmj8t7aPENTo/fZMFRGTQ3Z7fTRb04eeDw8PrUZeHMf20E/T1JJt\nXrv2EhaWyQlozrThcIptMOhji3X0Dg8PcesW1U/Mz89PKA4qFVzjNPsr11/GwR4dxDdvfGjThUuL\nS8j48PM939YRNRoNO588z7MCx8ZoxEk0dRtfeuM0ItYYqzoe6kXeLjVYMnTQeoGL2hyjz2aqmOXD\nyWQ5aj8m8dHNt2+jwumEtZMnLaP03/07fxf7DPlfXl5+ar0Wl0CllE3XGg2MRlyrMd5DOGSUcKWK\nd9+kWo3TYQ2rZ7kAbuXnt69wSA2AvHCmpEThrGkzqaXMqwriPM3FynwFXpeeY7h1hD/6o/+LXo86\n+NLnPwcAuP3+DVx/lahHijqo7c3H6HDblxfnMc/6aiLLcfsmXb7OvHAZXWa/n5ldRgGz3D3sopOT\ng5A3HJj6dGuxWq0irnKqRQGFlJkrJDYeEh1D74mwdZiLy0sTWhNjsLhI8zfTuV3/whiLRnYqNYQx\nre/bt+/g69+g8ouzp07jvbepzuqNL76KZdby3N/bx+EhrRsNhRXum/mFeeswwuTwGc03jTnNc4iZ\nIDQ7xoXj50BVUX9fWwlxtkGXhNB/gpkZcvK++PUvIRRn6Nk6M3jnB3Tx/9Gf/RMszPNe70pkI5r7\n5z7/6/if/sH/CADodBPMLdDv9o8i+B5dVpdn5zDeprmyshTgq18lp+GtWzex2ed0mFLwptQiBYDv\nvr+JOKa98zPpGAmzxI9SYPeI5gV0jjazrPf7PdRrfJl8v4u5Ol3YF1fPQXEN3862xPkT1K6dEQBB\nJRjL9X0sgdCfIl9AzOdO4Of46tVCYFrC8WiPD7MAKfNKhLqClJGA3UEOf/bi1G0EynReaaWVVlpp\npZVW2nPZpx6JynX+lMZZkXYh73VSaFsEDKRU0IrDnH7d8hIpaSBQfI8LwZGHWl1jtU1hyDgKoRkl\ncuvxAErSDTo3Cil/kxHMPQJAOpLU2kF6aZqjZALCIgqnaiN8KJaXMLnE5gbdoPccZT3w0WiM1VVy\nM7M8xdwceTknT56yd/udjcd4n4vkr1+/bj38t77/E/zT//OPAQCXTp/AK9eoQLK7tQmVsveuJCqM\n/NEjid6Y026pwPIJ+t2LV1+AYK4fpQAlpkt1eRyJklKRxgKA2MQ2xZRrY8fVcdRknIyA4nTeOI4Q\ncnTHS30oJox8NDrC20fkXW73KNJREQobI3r+VAM5d1CcZZOCXaORFb8ppfXQMxgk/DtpDDhqOj+h\n6boIMybdC1N0uOgQcoQhp8CWZ+ZxjosOx0eH+NEWRSTqlSVkQyaHrBo0W8U8zdDjlJ82Ep5LXp8x\nGtVaIZXiWYkF4QRImcw00RmCOs3ZmfkGBhxd6RwNEI8ibp/G/Pz0aJnZ2VkrTdJqtew4heOxLZy+\ncPEiZtiTr1Zr6HTIE75z+w5+8L03ARCooChC/eEPf2i9/VqthpMnOSVw4oRNdb377jv288Zo1JnI\nMU0nckFJktjocqVSmXC+wCBNp08hXH5t1WoC+pUAAaNAFWB3O+UrKJ8LtAFUiyJ2A5x/lQrm/+rP\nPkCWTWRfHjG68KMbN6xUxpMnWzh1igASvl+xUed2u20RheOxRpZy2YJO4bOMx8HOPt7+U/r+3voi\nOhe4cvbFn98+UehYCmNlcoQGHH9CAGpleUwGOBzlbEsoJt0N5n2EN2hsvvsvvouVBdqLGi9csQCG\nIirY6/YQh7QWXzhzBgvXX+YHEfj2t78NANjc3sMy90NNBbh1hyJUkQvcPmLEc9uF9qbbb+I4xpjX\nHJIQkhFbe6MuelymkMjMzrvzFy7gzv0N2z8LDFYwoP0eIK00wRNg7fQ5LK1QNOn+vXvY5UzGqZOn\nsPGQxjlPDDLm9BoNI9K8ArC0NI/ZGSaj9RwoRtxKKaGz6eep8hRyzgS4EhCM/vOEQXhAEdx3Ht/B\ndoX2jPDIwXZG0e6D3gHmVwiUcLq6jKXfpLa8dP0UXEH907h3BOaSxEf315EYWvdJ4qPic6S54uDE\nZTpLvvKFOmY4S7G25iKo0Bi6QYy8kBqSc8jz6VNd4/42DAMRDoZjeDXqN6NhJaQalQoWmQS16ip7\nJu1nKfaG1P8z2mBri87UNHXgFMhg2UVFUDTX0doScx9uRNi5Q/N+9Ru/h5tdLg/qvo3GPM2NvpmD\nzKmDIi3s2OV6BHfcn7qNwN/EJSrLYWzdhrAbpcg1knhC6lcg0Yw2dnEoKY/V12jqfQCZSCbpEGNI\nHBWgdImkzWiUAeADSWuDghZVI7e/JUTxb7T5pLwp0XvTd83u/gh3GRbe+OgeVlaZIK3ZtIzGgLCU\nAvW6B3DdQDgaIeXQsqMF0hF9/r0fvWUPnp/88B2MD2nAd3WO6ARt+vNBFZUGTcaj4QAbO/T5w16G\nIybnlIGLi0xkGh12cMgp1DgOcbRPn7/2xes/t33FxJYwcHgsM22QFPBuTLQMHaVQ57RGzfVR5bRi\nL4kx5AtfolOMMnq+3f4BHuxR2uoJHzwugJTHPcOkUCzMcxuSl04FRdUaIQL5WYwBCh2qjARJp7F2\nK0DEwpxSGhRB2jwVkJqJ9pwZbG/Rs248eYiYN3ept7BQp02qev4UJJPBpVGI7S3a+GCkrZuq1aq2\n/m55ZR4+43clFBSn//wsx+wsbTpepWqJZn0loZnk0HXURP9pCsuyDAFTFmTZBFm0uLSM8xcoNTkz\nOwuH03y7209wh9FnH370ET66QfUT165dg891DPsH+8j5krO2toYmP5sQwBELCo/D0F54levDC+gw\nF0rB54uH501Sr91u16bzCnTVtNZabSHg53FcB1VGExkjkHLdkJZAxnVlWaoRMnWKLxxcfo31yS6s\nofOIU7qeD5dpV1ozs0gySjtXGwEEUw7EaQTPobbMzjVtHVc4zi3ZsJDSKht0Ooc4YAZqPU7ghOen\na+Ax9G5R32AkIBymURAOcq5vUyZDQYSeIIfi+p2l1RPo8Pvxkx767HC5nmdpL4qxrlYDvHydUJYb\nj9YR8F4ys7wMxfPZ8ytYXaPDeH9rD61luti/9/E7GAS85j06C6axNE0Rcu1lHo3Qi+lQcxCjXeU6\nsDRGhQ9Tr1Kxjo7jumgzC75UypLI1moBcmaunl9awuUXqU23bt3CBx+Q43r5xcv48fffou9xfCzM\nr/DftuF71O5RNLIqAa6a1FzmeYZq0JyqfQAw7u1bQmclFSTvZtoIbG/SHrN34yHaPGdn1BL8GU4/\n4X28/HmagysnV9MOZq0AACAASURBVFFvfgUAcOGFz0Aw+mx+roOTl8jJu//4Flqz7DDtZTAuzY/P\n/OYv4vQ8K3+o76L38AcAgHplDY/v0zkUD9egeA+P83DqMaQ/iCxxZT8GqnzeVz0Xgi+lUiqr7+h6\nCpr36/nWDGIOZIzjEXI+I1eWl3Fnk+vEnvj44ivkeOcix+GY5neej6C5P9PhHtoezaVa1ofiC9L+\nY4WdfUZHJgJrbdpHzy0Ao/3pzozCynReaaWVVlpppZVW2nPYpx6JMjgWWVLHyA8NIDmN4ShlCZLS\nLAH4tusoAacoPhfKRo2EyaELwR0DgMPXSZYh5UiFEc4E4SMMXPYMc2TITVGoLKA4uuU7wmpchePY\nKoFPY/MzAUJNkaVbD7cwZJK269deQrVBHkySZHhwl0KkW/fuWpV313EQs+fcHY5wdEhhy3Gvj3TM\nnmYm0GDtJp3ngKRowsFwiN19inRs7R2hx2meSArE7KXWM4UH96jg7n/42/8zlmYpdJ+LFIqRCte+\n+Ld+fgP5qq2RI45ZriTLCu5TuJDwmJzyxGwDi23yCBqOh3RAY/O414PhcR0nCbZYfXunc4AjVs0u\nshApjA3DGyGsvIuGsYW8Qkj7WkoxeS2E1S2TQkwIS59h3VEPaTEXAhcLLqN9Ug3FofCV2SU0G/R+\nIiq4c5+KkHvdLvKE5tS5kyvIOMRfq1ah4wLRtmsjo0IazM6S11qrvIK5Oeb1yXJ4svByYb0ynSWY\nn6HPL821kRSkcnmGemN65fhKpYqcdbnG4z5arJF3Ym0NFcv/IvDgDoXIP3j/ffzwh0QuevfuXVx7\nhSKWFy9dsFxgt2/ewqBLEcTXPvtZeBw52lhfx8bGOgAgqAZ44UXKU8VJir1d8qJPnT2PWq3wHnM7\nbr7vHxtnMTU4AAAyuDA8F40QNqIpXIWkkNoxBjmDHHSacWE2EGuNudM0vte+cAF/8eBd7jcXIwZC\n5EZDFcSYUiDnOaNzA3DqolpzLGIRZrKHOcrBOI5suzL+2yd72zifTFfMateFFJjUQAhkPM89z7ES\nMkYbZMzlJIxCLaf+fe3sZdxhT73fPMDahTMAgJmFeWxvE4qrAA68fPWKlXd5+/EGwAW+nzu1hgsv\nkdZaFo7x5DFF4ne399DhU2W9v424yZEaqeEU4eJnWN59BIfRZG4mYAT12dqJeawuUZT/w5++g1rB\nRqsNOgzCCfwKfI6EG63hcjlBGsVWViiNY7gcucvSHEOOeq2cWMLKSSLcdTwPDheK16RniXKV6054\ny4Sw6bwsy5Cm00dp0miEIqwopYDiszDJgC6XEuwcOQBrgspqF90Nyhx8sLGPB/do3z9z7jGWVql/\nTp0+wMpZ+p7mgkZ9jgvvT2tcOE9j+OjDjzG3QL87c/Fd+GBZpHGIMRMD//CtD5DVmIOsfhY6pyhs\nrocQcvr95myzZVF4C8sVCwxrNpo4GrBuYJoi5mh6mCSoFFkMz4fhtdI5OoTHAJFG4GCHJ9i792t4\n5SoTK6sYccLtWnQxv0SfOdr+Z5hncJpSEtGY2vVoQ2LriKPgfhULLn2mEyg86dSnbiPwN5LOy2wa\nTusJCZ0x2pJbZllmN7UsS+EXOlIyh+GZ73oesrg4ED1oPpB1nkPrIrUnoTicCSWQZJML0mydq/7h\noMMImVQbNDgtEfgKMT9DOk6Rf4L89upCHS99lg6Yr8sGPv6IBGg95WCVkRxKedjepNDjD77zHVy7\nRhtQo1HHqCBnrAfwXJpcM+dOortLk+jBnU0EnK8Ooz6+/yMKP0ejEGHINSW5REFALnwBv6BoGCbQ\nXFR0lI2RMIndmRdP4yu/+MWp2hcxM7kAbB0GifuycKt0EDBdRbMaoMVpA085OGR250Qb6OIim8bY\n3KPL4mjUs3OiOCy10TZtIYT4mXgQY4y9JGutnzp0i/flMeqDZ1mcwaaIpQLqnGbKYw3DcOi5ZgvL\nTF8RNHwcMgpzONyy6K1ebwDwAVWrVdFsE+ptfvFksQxw//49uFyHk6YGOY+PzjTicSEoLdFmlF+r\n1bCb0Xg4sidpqz2Hen36TS3LMts3SZLYS8uj9XUL1d7Z3sYP3qTap93dHZtW++Y3v4kTqzSXHUch\n5QuJPn8ed+/c/Zd+a3FxEW986UsAgPWHD7G3N6FTePKEDshXXnnFMp9P0t6EzirqoKR8NjXI022U\nyLi2TWtj022eKywKNjXa0qUEUlpBulGewWen57NfuYi/+ieU3oiSoT0sg8CF4/ClV2tbm6mUsULr\nWQYLvzfIELBwuhSwtCVCCFvHl6Yp4mg6BGLCKC4jJYwqHAdYByUXGVF+AMiUQs6vVQYkfdpb9u9v\n4Ouv0tqvXXexxHQe41GEba4P+ugGMWIvLC/ivEtUFbWlE/CZaLLRmEE0Zq3A/SHWlggSf+vxOr7H\nOpKDIEXmFTwkamqtzqizhTrXjgV+E40ZcmLOnllFwGm1arWKWXYIHSntJcqRstCYRZ5mFjEZRQk8\nWayzFEsLtC5dx0OjQXP/xIkVXLpMtUZpliLj88CA5iEAKOnYsQWoNMHalGhnAFCuZ+lakjRFzMSu\nOksx4Hl6/2CMHb5Ari4Bhwf0+Y1HQ2zs0OevvvQEyyvr9Pyz7+DCFbosXbh6Assr9Nqv9zG7yDQb\nZw6xOMc1lb0NDBO6lO4MFB51CP3X8RoQoH0uTpTVtnOlB/MJrgynZ9qIeZ8QCkjZ+QujCOMhs8E7\nEq06XVaPen3rfNQ8H9VKoYGqITmFWvFdKzR8b1siGdF8aDZjsO8EJQ08pjNxHA+Cz8Lc5AjZkYhS\ngZjvB8NshPcjGt+be8AOo/r/qynbWabzSiuttNJKK6200p7DPvVI1GyzYqPOnuda/h1tzDHdKQPN\nH8qVC5eJQVxHQBbq1iJFaoqIiIDkcKzBxOvzPMdGljQEBBeDNnwHM+xhptrAY22cURQj4NC870pE\nERWaucg+kVcRDTr4/j//5wCAextHSGLy1rpHA1vIW69XMOZi6trcDF7/BfLSZ+fmkLHXnYYj3Pno\nJwCAcDDE/hZ5+FIZ+z2D/gC7O+TNSm1ssbqnpO0rKSQ4OgzPyTHbpj554fIpvPI6eZXXP3cds0ut\nKVvI6QjXscR7qcwm6VUIGzHs9hNolpwRysF6j153shwpP5/RIfY6lGKUMNbLc4pQmplIKBj7H3oM\n8dT7x/4Bk38ooi3mGBjhWTYONbZ2KEIS1OtYXqL50qgEcPm5fMeFdOj7vvSF13DuPCN87j7E+sfr\nAAAVJdDsqPoLLWwfUKqr339yzBOXWJlhBFx7AYYRZONxhMChcXa9AJKjHKmeaKvV5psWnEEIl+nn\n6czMDNJirfi+5dOZm5+3fXZwcIALXGT+8svXbYSv2+3i/fd+CgB48OCBjSC98srLlogyiiL7PUop\nLDD31OzMjI00Xb16FX//7/196rcHD/DFNygiEkexjeYZYyyHlVLK8slNYxlgASJJnoLpYiB8hZRT\n9JkAPI5QOFIj53mpc40E1D+f+eJlfPWXvgAAePPPf2yRtcpJoDnaLXKJjNFK1Upgo+NxPNF4VI62\nkY7RcPgU+WsBcHEcx0qYPMscBiHkmKT2cqMhOFqXmRwOtw2ZhkVcGINzVymlemrtPJaqFGFcrrQR\ndWjf2zvYwds/JDmUHUYYP15/jM1HhOQ7e/oSdjbp9Z33PkIUU1RbwGDEuqDvbT3AE0Y9qfmGnZ25\n1lPP1EuXXkDSpyhvHkosrFCkrFqrweO14nmejfANh0OELGHk+S48Rn4ZGJt6q1R8ME0fjDZYYxRp\nrVaFy3OtUqlY1HSapnYux3FskZdSShsxl1Ja9DU93/RpoDCTNoppHMdqRY6iEUbF2TMT4Ig5mqK+\niyL+NQoMbmxRJOdgAFw+xeNwYQBNb6Pz4AIWT9H69mZjwKFnu39rjKtXmGx6to2bt+lbt/saueLo\n3MxFZBYF6sFnYI00VRh3+riLgYTPvHdSKVQYGZ4kMVqsK5PnOfpMrBqOE7tPdMzAAgdq9SpqdY4i\nwsCRtLbubwJ/9Q5FKV+6mCJnrqfcpIgTes6t3RwbOzTw6/sCj/apg+4/Mcg41QsVoB/xHpxJhMfG\ndBr71C9Rl88uwOVUD6RByKzVQkoIPpzGoxRGMyuthtV0c11lD4w81wg5Bt9qNeBxjVCa5AjDmD+T\nIagUooXJJI8tBQY8OI5SaPFFq1n1Kd0IIIpDuLxZB75CrTI9Y3kaDjHLen9OOMRP3/kpt6Vqz3DP\nB4l5AYDr4L//b/8edYkQ9nA1uUES8yrQBsmYNuXA8zFmSgCtSWMNAHLpQfkFA7yCZCiOU9FozVMb\nz51dwck12hjOXVjFy6/RgddsVDAeEnrKeQZK3ufdp16p2sMniWMk3Hdpmtn0yzhKYPgGp2WOIaMq\ndK5tDZU2QAIaM0dKKFWEi7mWKRc2bUhCg/+KQ1RMXpifQZ0hYKbWsxoNQhwyOWQrSbHCVABOULVh\n/XEcI2Xkdb3i4YWT1JefOX8J0ZdpfDY3HyPlfvEas1h+gYS1N99+G+vrVBNX9T3Mr/ElqtGGy8LM\nxlNw+bLs+hWMQ9428xwBH55B4EEWep3CIP0Eoqeu5+HiRaq98TzPwvCjMMSj9XX+qRwjDrXfv38f\nGxsEHd/Y2LB6auPxGL/7u78LgC7sBQv6jRs37LidXFuzF612u40WpwWllDh3jtIG6+sPceb0GQAk\n2lugpMIwtJe3MAxJf25Kky4geNEZrZFyukWmCmlBDusopMWRLiaksY4S8PiQqLfr+A/+4/8IAHC0\nNca77xBq69TZBTiciu11x0gYBRsnKeIxzenOUYQoLASpqzCMsIqiyE5l5SicZP1BbcxT6cyfZzUm\nAIWSlm5kpFMkRapca5tekMpAcN/JSgW7LOYeHOwgHNDYn28tY7lKdWCxDjHPzN//zr/1bwIAXKXw\n+KeUnnsMIGLi09VzJ3Hm0hkAwHv3PsY//fM/oc/kh3Bm+bBEDs2IOAkJ15nuuGk06oT7B5CNhb0s\nOY6yadUwDO1FdTSakNSurq7CcQr0tbEs+47yERcoaMezwtfz8/P2O7XObVr7OHkqCWJTu13XPVaL\nKVHlmqXBYIBef3qh7CTJkOeFjluGESMkB6MBwCnQ5fMrGA1YzUAnhe44ZN2zhLK9ocY7t1js+iDD\na0wsGccfYDCidjnVGjpDau/Onsb+AaX56idagKL33bpEhZGPqalCs7JInuXIWazbc6uwObMpzPM8\nuy8LIWydse/7OMFC5bnWtm9nZmbsZTXP8wlp7LF6SSGk3fYPhxn+8XfpbJt9twrXK4TZU3RDanun\nm8Cr0gE3t3wSM6dpfF9YiPDBxzSvazUXUbFqcqDdmJu6jUCZziuttNJKK6200kp7LvvUI1GBKxBw\n5EdKIHAKbhOBAi7higSGPRYhHRgOWTiusFGKJMnRL8jylLBaN67joMKh5EF/hCgpitdim7rQWlvC\nz8BzEFTodjwzM2O9iv39FC6TVVJ4fXp0nuM4qDLvzBufu1KAQPDOu/eQcJFr4FYtJ87+fgfhHnst\nYoJSzOFYnTilJETBdZHk0Dl5vAaZTXcKF/Ar5M3UGxlOrFA49uyFE7h8jcjvrl47j5oNnWbQCXkw\nWSyRMHKs9oz2hex9pmlq9YZ8z4PLESpHKYu8iJOYuJoAGOnYAmSjc5ta0AbQhUcjJRz2Iovvi5MY\n6hiKs9BaM+ZYZOnYayo9n0SrrBQHjI0yPMvSdIiIUYJRNLKRztm5BThcTJvmAoq5gBy3ih4jPTQk\nAtbIu/DSVaQZE9LlGr904pcAAG/84pfxeJNQaf3OAEuMyJtbmkO1St8pPNemZbQxcIpxVo5dB0JM\niE3zPJsAIJxnR05nF+Zx+vw5bm+KXS72fvvtty0x5tHREboMBtjZ2cFDjlBlaYp55t956dpLaLYp\nFXzvwX0cMEHjzu4u7j0klNbS4iKWOV14/vx565Fubm5ihtt+5uwZNJoF15CPKy8Rd8/6w3UcMOGg\n4yhIM72vJ70cmtE+0gMxaALIMEmJpnmOjAv1XUfDk5zOcXwELkUCvbyBl6+THMp/9p//J/hf/5f/\nDgBQawq4PqeuOxEe3qcU8P7eEcIhfedopG363XGERX8laWpRjedOncZXv/51AMD33nxzakLReMh8\nSL5vJXmafgXg9Isl2gTgBz4SQ2t3kGnsdA/5+YYYNDhaFPbwBPS66tdRP0uRKJelODxImD49f5TF\nENw/D6N9/OhN5hDbuosN0BwQcxWkpohQpxA8dspxIfR04+g4LjyO8PTHoY1CNBpNpBGnSZWa6OIp\nhTbPx3q9iYijg0G1ZqP8WucTlLHWNkY9OzuLWm2yAxa/lWWZ/X7f922/uq77lGRXAXqo1WqYEnwI\nAJBK2LmQ5wlEoYepXGiXMxB1H6oozM+1RetW2xpZznu6zqCZr+7wUOMHT2genRcuzvC+2lQNpBzB\n9Fd8mDaNrapW4AfUb1ooaD53jRAwHOF2pLS6O2E6wicICgPAU4Cfoq+01lDF2SFgo09BENh1kGWZ\nPb+TNLZnQ6Yz2/9ZliJnlHuYt/Bgk8Z9r5/CcHRx2M/w279Gafnf+tVfQY9LBt7/6H3cYCk1LSpo\nNfj+odxJOnxK+/QvURXPHrwAUOHDCVJaLbHcNRj0KZytdUgCaADidEKPcJzVPI5jJAy1z7LchmnD\nMEKii3BmYgek2WxCMfRaSG0nbxhFdtEYAUgLsfbQ6XSnbqOGgMOpNI0xrlxlVtRRD+v3O/w8fQwG\nxaXOs0RrgIEoLo0ihSkIQnNh8/lpKmCYnMx1DTy+BC4v+Di1RuHJixcX8OKlswCAtbUT8GsFYrED\nyeg/T1QwKA7IvQ6aLLz6LOvHnAoVEkUwNcgzVHhie45rUzdaa7uZxFmGoqBJHaslwDGiR2GUZRIu\n0rsVzzmG1Mvspqi1njDeA8eY8CWOX6KsXpYQU6NJhAiR5tTOzccH2HhCaJ+11dO4cJ5SYO1mAIfF\nscdxir0ha8/lAousYbfSUpgNOC3phDCMDAmcNhZXacNqLwAeb5S6pmGqvICFgikKXQSJpgKAMuZY\n5lJM1o2WyPPpLxiXr16xKSBXejh95gx9T57jJDNOP3ny5KmDoZinAkxFAqovKR5iHIa4xumWZqtl\nUyxxFFkqhjRJ7GZ6/uJFnD5F9SiNRtPWo5g8x0xlUvvyg++TEOxwOEBQmR6B6Acu4nRyodB8AY/S\naILyk8aKy4o8g1D0DEK6gKZxXPWuoq0prP/aqy+j/V9/EwDw1o+/gyePaG7UZAV6SO3t7naQsWPX\nnnFtyj0cJ7ZP2nMNvPwyMX7/zu/8DvaZ7HY8HmFmptAOfYbx2orCCJrTIL7nwnPYEXFdW08215yF\n06K+OxgPLZO7azQSFjzei3Yx5EuUq3uI+LnFkCHuYQSfa0dyYbDLiNTdbhcRP4uuSuRNRj8nGbK4\nIOd14fHhrbMcUTJdyrLdbqO7QzVZUgpb8zoaDiF5PcdxbFNplUrFpkkbjSoEO2hZntpLlIADdYyh\nX0r6W8/zaT6DDu6iJiqOJ4oMaToRWE/T1PZvHMd2XmutrSD0NKZcB4ZpenzlW8RZpVq1NbK+P7lU\n5MbYuQzA7p9aJpC8l/how81ZCcGXyFr8zPUAPpP4BhCoV4vPOOSUgeoIdVEzCIksL2hlpJWp14B1\n8qYxIcRTl6jiFiuEtqk9YyZnvOu6TwnZZ9zPee7b0pwoTpGkxXmfQQq61LdaTUQZraejwa7VI3VV\nho9vUJnAxr07ePiI1DFGUYzmAtc5qortB6UEphWtL6xM55VWWmmllVZaaaU9h33qkagwTpFwEVyW\nZZZkEkLaItQ4Si06QQhpgXGkbTchJCtQXL7vW+JCILXeuxAKktNHUipU2ENWjkLMqatYxzaaIbud\nY1Gp0AYzlJoQe05jaRojS9nLMjlqdfLcvvG16+heoyhKt3OEXp/6YTQySNgrjKMI0Zjez7N8EuI9\nVhTtCA9VLlxfWKris69T2uPlVy9jkbXcGjUHRaw1iUKA0xUmi5CBCkqT0KD7hNIkw8EQ0QylIlqX\nfvvntq9TSCoIAaco7EwS1Ni7qbq+LTgXEDadl+TGeoiO61hiU6Ezm6LTx5B4hbfhHCtAzTLYOaN1\ncsxLOJ7Ce7qwvHitlDOZb88wo2ObRu4MBuh2iS9nZ2cfwz5FJS+dW0WjRZ5PL3GwM+DC3lhhbZFS\nqeLUDCrzDDJwJWJOJ8ZZjhF76EmWQ3Pxqw+JIevnpGmGhPW6HAl47J1WKw5cpyiWh10TrnKs9uM0\ntrm+gU63Y7+nUkSBtLaFnmfOnLHpPKmURciMRiPsMnrxyeY+lpcpinnp0kUMBjS/er0efPbqZ1pN\nC6oYjUY2RD4cDPDhByQp0m63rUTHcT6v1dVVNJuUHj06OqRU8JSWdwTMoCBbdeyekaep3TKUUFZn\nS6cpkkIOxk2RZBTdSdpA5lIDAr+JRp1Sk6sra+juUXuzJMU8S/Osnliyqa7Hj3cw7DMhqjY4d4Ek\nUX7lV/41/MZv/qbt57/93xC4JM+NTXs/y7Qs5oFAQYgUZglGnMJUiYSKmWhw3MfMIkVWVs6dQLXC\nkYlhFxkTAitfwuF0svF8tCoTxBsAxMMUByOaD4f9IXRBErzShOQoaJQnSJjnKE8AxdEBBQcpc0nl\nWWZ5sZ5lm5ubFp0nMx9VlrZyPRdpNJkLhW5ht9u1KDnf96wUUq83hlfIKHlVSxRsjILAROuzykXm\nUkqrIdnr9X4myevxqJTW2u41nuci+wRnhuO5EKrYsxTcYzqIxblYqzbs3piY3JaYGDMhHXaEgs9l\nHwoS1Qo9czUAAo5OVjwfBT+s0ZkFT2Q4xlEnlN23U2OsPFGqc4t2zc2EAHiqNjrOXwP8TN636zLP\nnurn41mEop/zbELSnaY5oojOtsF4iA9u0l6ysz+P4jwY9I6QFoAKIXDn7sfc9hxOkaINqjApa+sm\nY6A4Y4yCcqabp7Y9n+jTz2HdYWjDyITJZULLPJ9A0Y2xYVelpIXRS6GIMBEAxAS6DuS2BkeISS2N\n63jwA/7+NLM1P1EcARzey/McCS+y47nuNM9smDBNk6k3NQCoBQFEjTciM0khOI5E1We00ollm3t3\nnIolhEOeQzPTrRAKBbbGKNemFyt+BRWf3q/UNFZW6eARrmOJ0HIAmlNHqlmxQViThIgZEYk8QW2O\n0gb1RjChFHiGFQK5qdaWPC3ONXJOMaY54HEbKq5nL0GOAwjG+2tgci0UxtYAZCZEVBCnCmaWzV14\nDIc9rm9Il+XJIhPHNmXxMzZoozWyKfF5eaoxKBjfsxySLxhRMsbGBjF4Z/EhWrzJpqaCKKdnjDOJ\no5wOnW0vgknr3EwPKfeR4ydwWfTW84VNheqQdR4BHIUGY14rCtoiRGdbAapcG6EEaDcDkSx+kgV/\ntL+P/oAOT9dxEPKGFYahTRvEcWyRYlEYWe0613UxYDRfs9m0aKg7t2+jyfQhniMRMrIvGo9Rr08o\nNCL+zjAMbQ2EUsoitqSUloiy3+9P0ipRhNFoMHUb/85/+o+Q8Rx1j+nxpUlqL/TGwIbvjcwnTphx\nAb4YrPh/gUDQWsl1jv6QLpBpdoADVgmIxjkU11OlKaUMAaDXGdvDyWjY0oO7dx/iH/xv/wgACZK/\n/RM6AHrdIcJwOrLNQoxbS4ECpqkdAfA45Vrb9EumJXZ36TLSGw2wzA7XyZUZOFzDF3iBZT7PHY2c\naWQKKg8nACSn+DxTRcEzKcxESUBkAi6zWmcG0DznkzxFkX8U7kSY/FkWRiEqhWaiUTat7Tgu9jrk\nBM7MzNixPT5/iQiW02FKWJLiJE7t+1GUoV7j580y+FxbNhwO7eVxNBrZi9lxyo3j4tie5x2rm6pA\n5tNf9o0Ux1L3EoqJi2Wu4BQXPE4zA0BVTYIOlDqc6PcVe4krcgQcgaj5LurMuG60scjU0CSI7GVP\nIuNx00IVGiDQyC2iNDPCnkm5gb1oTdXGYx89rjxANBFF4OPpzxw3q7OLyWWsNxxhMGBH0EQoSOJ3\n93btPaDiSPjWeZaQlUm5hE3vSgVYRGtiS4W01nA+QQ0m/UJppZVWWmmllVZaaZ/YPvVIVGcwwmhE\nESEpHCi+XcfJpMxfKQmpitu3mchvCGUlGYQQk2I6nSPjm3KSZJhEJyaEk+pYSZwrDRwu+owSSWx7\nANIss0WLURRjwFwdaZIg/QRehev6ltxSKRxD9hlIVYQVjU0XwQltgahUQHuGteZaDathJhzPFir6\nFXdCFIcUWhepwARCMCmhlMgKHpxUQjCiLBcVyICLm6tAY5mjADpDGk8HtfhZSMXcGIQcMk11bulD\nfMdFncPj1WpgUUrxOIIuvNJjpJmAsQSshYK8FA40F8+7jvNU4eHT0UthXz9FYnjs/UJa4VnmKR9V\nl7zWuWYdLqcB2o0qqtz3Os8x5tR0pQIsVCfeaZXJ4EzSxf4+e7NDRRBKAM0ZFy0u9PTrPqTkcYBv\nOdI8aSDYK1YAAp6zWayxx4SIOs+gLKmqQbtFcycInoWxBObmZu14VKuB9bRnZ2dtkW6v17PjkSSJ\nXR86z9GsU1rq3LmzNoJ75/bIpgJ930PIIIYsz+Exn5JyXIw5Zd1sNnHyJEmEpGkGxym8RGkjtaPR\nCCsn6DuzLMGtmwfPbFth3/uDuxMX+Lhje9yBFsfeUHryb0ZM3jcfWDSZgYJgr1WIfEKaaiQKhLEQ\ngBYTMuCJfypw/y4Vot+9s4fj5KhFKlZIYDSaLhIFLvLWzjG9SMexZMVSGKhCpsNVMHkhtaQx4NRa\nIozVlMskIGUBbFEoJEPjhF6EUYxwzFx6IaBTnud5itSipAwURzQcGKTcD8qFRV1T+n66SJTnV+AU\nhcFuDfUmRQTDOMXN2xQVTrMI3QGl2QUUBkzY6DieBaqE4wSNOq8tbyJV5Lk+Okc9fi6Bw0P6nm63\nY9PdzWbTCatGqgAAIABJREFUzkcqXKdnH45GFmW9trZqyy9GYWjJU6cxqdxj3Ec5RAG00cKWOnhi\nEumCMZY3SUBAySJjkUNWmPDVDVAFcyC6DTj8PYNxHyFP60QF0LbYXiPnVKCGKALcnDYoADwCkJMz\n+BM0EUJI2xYhzF/7N47QO+qZylxG5rYcJ08TzLBMTM1Tk31fClucr6Q7WcYQk0wGQeHp40rZDIvj\nKLsPua5n0fJTt/OTVqKXVlpppZVWWmmllVam80orrbTSSiuttNKey8pLVGmllVZaaaWVVtpzWHmJ\nKq200korrbTSSnsOKy9RpZVWWmmllVZaac9h5SWqtNJKK6200kor7TmsvESVVlpppZVWWmmlPYeV\nl6jSSiuttNJKK62057DyElVaaaWVVlpppZX2HFZeokorrbTSSiuttNKew8pLVGmllVZaaaWVVtpz\nWHmJKq200korrbTSSnsOKy9RpZVWWmmllVZaac9h5SWqtNJKK6200kor7TmsvESVVlpppZVWWmml\nPYeVl6jSSiuttNJKK62057DyElVaaaWVVlpppZX2HFZeokorrbTSSiuttNKew5xP+wc293vGGAMA\nMMZAa83/IgCj+H36t+IzT5mcfLwwY8y//DkAEOKp/y1+iz4r+CMujC7+1qD4muPfKaW0r6+cnX36\nS3+Gbd9fNyZLAAAKGmmWAgDcSoAko2eYXVxCpVUHAByFA3zvrfcBAO/d/BhhNgYAvHThMsbDHgDg\ngxs/wuHRJgCgPxzi4YPHAIBRdwipadi0AWTFpd/yfdRaDQBAa3Yer3zmawCApbll+Iaa8Itf+QrW\nFhYAAPXAQ9WzTfu5bfxbv/dvGADwlINmjdrguz6SPKM2OwqepK8wUYxsPAIAHBzsI+cpVm0vQvtV\nAEBQrWFxfgYA4IgUYUrtl25A36EdjMd9AMDO3kNkWQwAmGufQTim3xyNjtAf7AMAOntPMOxSvyWx\nQcbPnWQaQvoAgO3tJ88YR23yLLfdYTCZI3FMYxuGERoNar/rSkymoPr5X/0pmlL2t585T1///QVj\nHGqjxDLCDo1NbzuENtTfWRAjqFAb5+ZSBLMV+h3dAqIQAODoMQJJY9Y7cKGCRfoBJ8delz4T5x6Q\n0kg0VICzK6foOxtzqLdPAgAOtvvwQlorWZZOmiAVYt4mAiWwfUBz/1vf/tYz2/hf/Je/ZQKP2mW0\nhuYx1doAgtaKhoJULn8mRRoP6ZnjGMWGEwQNCHgAgG5vBOX4/H4dUlKf51pD8DwxOofvOPyZKqr1\nJgBgHOf4yXsf0Pf0x8hNzn+bQ+vJs+UZ9dWN9z74uW3MkRkAGPb6+PF3/hgA8Od//H/Aq9Da/9ov\n/+s4c/Eqd2MFnkvP7VcUdE7PKoQHI3hfml2C63JfILW/Y3jPCMdj5Dk9Z7VWs/0GowDBm7MAwN8n\nkNk+FMbBZJeevHIc5+e2ceHFedNcot+RdWDnI9pPvKACzb+TpjFMSN+paxoyo6+syADJ/8feez1Z\ndh95fp/jrnflbXdV+0ZbAA0SO+CAY8nxO7sj7Y5iRrHSrjZCG6GQIhQhhV71pv9A7xtSSLGr4ewY\nLmfIGY4jSMKRQANoV+2qy9vrzfFHD5n3VDUIoqsxBGc3ovIBUX1x7rk/m7/8fTPzm9oPEwPLlLYU\nygV+4/VflnZ5bb7w0kvyfCbhve99E4BuNMr1l16WZ+IAdJ631h7zne++CcD0RIHYkPfXZs/gd0Q3\nNbeW8VXx/Olf3H/mOv1g6WHySWdeHMcw1KWHzqTDz1iWka6dJDEwDGmnZVqEkW6cJMLU83DQ66X7\nYGxkNH1XaPCJWiNJklT/GYaBofNsmgahvuflF84/s49AMlw7w3cdVQ7393m+95N8p2maR/rhz92I\nOiwyIQft+gQz6ON2EMmP6caz3vPxZ1IjCiNdOEnyo7/3o997tsycmCcMZDOZFiTEwxcRRfIuy3KI\n1OjY3N/i5kfvAnDrzl06fVHi7d113IEYA7v7a9gZVbhhSKyLMYkTXNeV3zItbEsVVgYCVw77jbVV\nrlyR90xNnaa1Vwfg7fe+Q+bVV+U91igrW00ALp5c/NT+eX05MEPDoGCLchspVcgaoqCx7fQwD22X\nbEGMpb1GAzOQdlu+R6Kb3TVMem4RgHIhS5KE2jftCwamHnilUo3BQNqZtR2qo/K9qJrQ7ck4h5N5\nlu49AODR8l564NkmJM+2LVRMTEsVBwdryjAclpYeA3Dr1h1+8Re/DMDY2Mihp/7zAHRzhRyeL3MZ\nE9Lpy3pptnrMzcshHDgJRT2Qi3aCk5WxTKwqhYkxACyjTtgSA7/gOwQ9Wb+tXoDnyngHPlQzYhTn\nOz7NusyPMdbnxGtyyEclCzeQtRnHMaYeeEEYYerlIwZM48ft8B8VxzSwh3alYTA8U6IEDH1PTEKi\nh5CFiWHJejUcB1vX98mFU+zuStt6XZeMI6qyUi6AruOt7TrtjvTdSGIc/eFstse0JQbYiYVFSrUa\nAHeXHuDrHnUHLv2uGAe+7xN/0qXwE8TS9dxurPLWd+XwLxdDbEve9dHb3+Th7R8AsLbVZGZ2DoB8\nIYPrio5KYoPqiBh511/8Ihk1orI5i4zOd6QG15PHj9jZ2QFgemaOCxevAVAdncbJV7RVJsbwImHY\n/L33hWtQX5VxDWywjKHRGhAOD2XHILFkcr22lxoeUSkBUz4vJkUiV543CgYTYxM6Li3++C//FoCp\nkQKlWPSPS8DytlzMmq118llZvwXLoKOXg8T3mZoRHTRWKnD3sayRZjtgbqJ85C4GQUCohrNhGE8d\n8KauI8Mw1LCX/XFwLiXpXkkSg0gv8HF86Pkkwrblmf29fUaqVQAy+QLNRkPaEAdY9oEJMHx/kiRE\nCkAYlslwPh3b+bFn7echPynj6fD7Do/zT8pQ+wc1op42f9TiJkk/NoxPPgR/pMNDa/1TnxsiUcBh\nMCwdyOTAoDKOfvQCkLFJ16KZpEZUAjjJcEPYbGyuA/CXf/MXLK/eA6DT2SWXl5u/H3TY3ZfDqddv\nYep1PIoOlJvv+PgD2XxxFBHrRgz9IFUk+UIetyeb+w+/9v+ws7UFgGPZvPHD7wBw4tRpWm1RVP/n\n//K/f2r3Tp+/IO3wfAJFZfq+h6kHC5FBGEhb+50Bth5WpVKJ+rYo4FZjl8ASVMOzszTVMJscHSWf\nUwMskvZgGKmSyGeKGIH85sSox6WzoqjGRxYoF6VdphGztiHK/Q//5Lv8zXfuyPhYWcLo4Hb9adJo\nNKkoypQQp7evKAz54OZtAD768BZXLosBMDY2kt4GZd38ZDf8UeW5Nr+ZoVKUOUjsEaqKlljBBhMT\nYkh0owDT1zXrm5yYPQ2AZxXRM4VieYzdFVHWrp8Qq2Ew6PiYyPw4sUXcVnS2G9LbE6P+zNRZ8ron\nsqZDoPM8RA8BLNMkjGXe+r6HmTn6YZwxIQVYDUgs/YdlkcnK4RdGBgNP3h+FCeWCjEM+l0/1zezU\nHLXKKAAXL1ykVJJ+ZXM5Oj3pe+HhBrfu3Aeg027RG0h/vb0GD54Ielb88ANOnFAUbqTMWFUMKq/f\nZ3VFnqk36ika9CxJEhnHux++ye6WfP/UyQli7Q+DDuubYggsLW/Q2pdnxsZqdDpDo83lwvlzALz3\nvQa2XoBMOyablfXR1WeDIEjbttLeYnftEQDnLr3M4gXZc7l8DVNRO8MktZ0SI5ZL68fkWWu27/bp\n74p+ICIFeu2CRRIo4lTNUdQFWR4pUhyX9TtWHaVsyzyXqhm8vsyVFVjMzwpiurz2kL994z0AXnnx\nEi8vKFr9ZJ87y2Isba4t02wJOvvbv/Iqruq9Zi/LC194BYDdToblDdFZM1Ml3rq/8an9OiyHD3Db\nttN/C0IZ/8gzcDBulmVjmaJ7PS+g3Za5yucKJLE8YzoOls5buTqC2sQ8WVkno0gtZpJeHJMkSd9v\n23aKYlmmhWWZB888p54b6vHnlZ+0ATWUw8je8Dc+blw972//53GFPpZjOZZjOZZjOZZj+U9MPnck\n6rBVdxiJEmv7AJ48hFRimAf/MA+e+OQ4KHmxPvTJlvvT/ytOXfny4Y+iYYKAPR9wGevzfujT6Mht\nMQhCJmrjAOQzNt2e3Bju3L9D323ob/kMIwTiOCRUaBYMLHUJRGGcxnE5joNlyjNhEA1DEYiCAFNv\nDF5/wHvvCqQfJzF9RX2cjMPO9h4Ae/U2u629I/Wt15fbWRyEBOpK3N+vp2NaG63hmHLrsS07dWuU\npqYJPHm+22pLnAFgGhlCjZdptToU8+ImslM477Cf1ebkCRnDL7w4yompko6bS+zLGHrugGpFfv9L\nr7/CzVuCvDUaLpY1hB0/Xb7+9W/wyhe+AMDZs2dI9GurK+ssPxYE0XFy+P7B/Bxcyg7W9cdvNMN/\nfx43qx+7H36M1EZnMRQtWbjwIidPLwLwdvFtgt4+AEHdZG9TxtUsFhg01V1VdOj2ZB3UtzoMfPnt\n2vgM/Z64W0edMiCoTtCImLDkhr+1eZ8klLnNOXl2tzcBiO18Op5BGB7cxqMIXxFEs2SzOHf2yH10\njARn6P4zYqyMrMVqpcaMxmX5UcLurvSx3RlQzA9jwCbI5QXdGB0fw8nI/rMdJ9VJYRSRyUi/ggWb\ngSdo5NraKq22jINhmeQSecYwEvZ2pL/txg6trI5DxqI4RPZyZaL4aHP55KHEV73/znfZrysCURxj\nvCRIzKC9RaJ7a6xawO1JP/cjjyEE7zjQawlCvHTrDqNjMme1WpGJCUFrNtcFVSkUCszOiUtwa3OV\ngbcMgOf1WVsVN/dLX3idqbnzgLgKD2Klkh91DxxBarUKcxOz0reMw+K8/D0yPo1pSx9m5ic4syDr\nYnJ6mmJFYxUtm0e3bkkflpe4vS7I2drKLnlFZgrZIsWyIG7jc/NUK+o67O8xf0EQuld/7gbf+nf/\nAYByMcfLN24A8Gh5n7mJBRnfqRkunZH4vqBzj1OnZo7cx8Q0CDS+yDIP9GEUhhyoLIO8rkHTNFNX\nXa/j4QWyF113QEnnPlfIEmiMVhgkFAuCyLl4NBqyNpPYYGxM0NByJYOl3oTDyEwQhhjG8HPwBnp+\nWAbFYuHIffxPRZ5ylSoy5vs+Gxsb6d8TGitcqVQOx5keSX4q7rxPOmBs206VUZIk9Hp9/dwi1kCG\nOCE9TD/1EPoEd96Pdx3GT7Vn+IiEEh8+AI/evwiIVMmu7W1w/9ESIEbO1QvXAViYXMANh/7qmHZH\nlNtg4OH1pb8D18QbwvKJQdYeBlr7jIyKT3s/aIAaS45hMVSMQejha9B1oVAi9gP9PCTWuKxadRKv\nIZ93mntkykcDIhON5TKSmFxelI9tOXgD6Y9tmAxjRS3LwNBFaGQyTMyKYgk9l766IY0oxNA58/0A\nXyMyc3mNx4gjErViikWbxQVRBmYc8N67DwG4ff8erYYc/AYm3YEoyNXtkEJRNkQYtWg2G0fq4/b2\nHl/7gz8B4Ktf+VU8NRZ/+IOb7O0IrF+p5QlSI/dpGQZQfhwmfnqt/cO4/IYS+A7r91cAiDObnDg7\nBcDkXI7WlqiCdt9hQtfaWKXAvrqCB+E2sfa914vIyleZv7JApXoGgJWlR5iBro/YZtKUvzeSVaoT\nYgiHhsHmtriYCrVJgkDWo+sOJPgb2R+RerdmT8xTHC8duY9iRGk8h5Fg677M2SZ5NagmKyNMjkl7\nuj2XWF0gxWKJqsaOONnswTxaFoG6zQM/IAplbWQcOL0oBsbkxAjrqpTb7RaG7suMbWKb0i+LGBMZ\nQ9OIOayxhu6iZ8l7b/41ALvbOww80Q9/+9Zd/ud/83sArNxuYWTEKLiwcIJQA64fP1qnqoknvX6d\n1VVZB+2WR7fb1v6fIpeVQ3tyfDz9zUpJ9l8jZ1NSYyXyWtz/UObRMaP0QjExdRrUgPysHu5zV0+x\nOC9rynZMFqZlsV26cIWlJYmtW91e4/0lcdsHP/yIVqsDgGlHnJmRpJWxaoV+R86VbCZmvCaL6tXr\nZ5muiu47fbLGVF6erxRNciPS9slZk7O/K5cqp3SSjVUZo1K+hhOJ8Tqor3P+3CIAVy5eIZ8/+nEa\nRlF6WPuBfyjh6sAFHQURsR/rODjpRT0IE5p1mWPbNnE1OSObjamUZf36vo+vMXBREKZGmusGqSFU\nrmbT3z0ck2iaZurOi6OYUPdlmCT4eiH/T0MOEsY+LkNdYhgHhlMcx6yuypp99913ef99cekmicHv\n/Z7sn9HR0eduxbE771iO5ViO5ViO5ViO5TPITwWJGiJQcXIQsN1oNHjwQIIyLcvk6rXr6d+xpgGb\nCWmwdPwxr8wn3erN57zpH348Sf/zfCgUwL/9s28yroGN7fYGa6vSrySw2dmVW+vM1CZrawJ/B/0B\nOU09pmDT70l/+z0XX2+kgediaDRgQISt9ACJZVGsDbOkTDxXYNpqrUC2KFb0SGWGa6fFddFtd2j1\n5KZSro2z+kBcUzkz4sz84pH6ZwxtbcNIxziXc8jnNP3dMtPMJ8exsZ2hG8SkMCo32l6jReCK+zCK\nIgJ143h2Qqsrt6piWeGNKCFSmoiJkTI5Q25U3/2b7zPoy+/sNl3cUMZwpDaKoQGSObvP7Ow0AHeX\n7qXBlc+Sl2/c4N//O0kZ/8Y3/ioNtn344BG5nNz47UyM4xzeMkOU6SAQXVzTGtxpGilCNUyL/4eU\njfVtWupGfu+9tzh1StwktlGl1VmWvx2TX/2nvwZAsVTk29/+I/k8cOhqllS1DLmijP36xibnF8SV\nMzE+wto9meNaPI6VEdRidGqG4tDlUBtna1PQrdBok0TDlP8wzV4NogiroCisDe/+4K0j99E2Yhyd\nC8MUVwzA3NQk5YL87Vgm5aJmWI2MEA4zkUyTgj4TRk9nQyWqhxzLwNGpzlgx+awG41p5TFPWXb2e\nZaAZi2YcYUSy1gV9HWaxHQ41SA6y254h9+9+CMD+7g6r67L32/6ANQ0yt7IR+7syB7s9jxMnBNGZ\nmp4ik5O12/O79FvSvnIlx9iY6JP+oM/ND4R6ZYg4NxoN9hq6b2OTZlMQmfHRMUqKUN3+4E229yWY\n/Re++s+YOXlRnk/Eo/cj8gz9urG7z4ff0cSbZof5kzKu/+v/9j/x3jsfAfC1P/g6xXFNVOm4+B3R\nF5WJMv/8d34egFIGsl0Z+7HaCBNF0SOnJsb48nVByI2gTt+VdTczN4et82Dbefp7sn5vfnif/cfi\n/jSKFfY0IcfY2iQXyhz0BnO0dM7/9Zl/8ukdBIhjDqf/H0ayY/U0WM4BRYQbHCCVlmWQHWbwmQaD\nrrj2Qi8kO8ymTUIse0g1kVDVuXLMHgVN5CEMsVU/RkmSJg0ZGCTJwd+2hpVgWiTh0RJ1Pl85cNcP\n/xRKjuHCMhmyE7jegPUN2Rt3b9/l7t27gCQSlUoyv1evXmd6UtzYOxvrZPMyVtWR2pFa81PNzrMt\ni6zCxWEYsrYmnXv/5vvs7skmvHL5MvPz84DwsETREJp/Wsn8fVwjz/ru87770fIDmg2BUfOZLAsz\nVwBxc0WB8stkCxgK62/vruIqN021OoFtyIHhexEjVVEYg8GAjsZWXb5yjaJmUi2XVnE1riWJQkZH\n5bsD3+XSFaEvqFZmKenmyDkWG5syzuvrT/hn/0QOyId3HnLr5q0j9W9oACRJnHJOmKaRLtQwChgm\nYZhWLt285iHqg0JthPqeKGPrUAZKFCYMtD+xboKMY6EeSGZmatx4SVwmtuWz/FhcpecunWNiXrLz\nOgOPDz6QDDoj06I6InNR3a0yOKKbZO7kXMrjNQgH2JFuDdugH2gGTqnKSAr3JgeZMKZFqynuhI9u\nL9FVZXrx4jnm5jVOIokxD2eIHpAo8NPK7NvZ2kYTzuh3+2ysyaFy8fxLjM5JNud+c4/KCc3Q8m0Y\nxrpFRfyh69jbprcr8zo+aXLvw7cBqDk1LE2zK+TKTE0vApDLZGm2xXjrh5DNyfyEQUgw5AgzTRLk\nIPFDj4Ipimxnc4vdja2jdzIKMXReMrZNRek2qqUiiV7OksjHSNQwtm0czXTCAGJdL/GhzGDTwtT5\nsk3IalxTCRNLO9wb+BSVs83N2BAoz1Fi4Wo2nzsYpHsG0ziU6WQQhUdzCrSaMk8ZE166KpmTa3vb\nrD2RfXHlzDgvlET/3Hqwx4e3xG335Z+5nhpDhpXHtGWOO702hZLoKCcupBemYYaxnTFod8Rw8jzo\ndWW+9vcanDwpenrgeeyoa93O5PhHPyvfnV84n3IBgnXky6lj2ExMyQF26fJZfuZV4W6aHB/lN3/t\ndQBevnaKOL1gm7g9mYd8xqGQl7nqNrcINSxgfb3BN/5a3DelapmPPpILrTvoMpqXPtXrAaVRMTpP\nnz3DrY9kTHt7G2RjWY/dnUcUNYYsjgt846/+SsYr8XjzfYl9+9f/5v94Zh/jIDzIirQsZf8SrZBm\nkIXRAXchB+dStZylqpeDTrtDU+kXivkCvoZx9HpdRtQIGKlU6HTE2ItCKKrbsZDLpToJDCxbzuY4\niQl1rwRJQqJncC5OCHfW9fmrz+zj5yXDqCADM728GsaBC6/dbvNkeRmAh4/u02zJnmnUG+mFYX5+\nnkpFs3ILNm985+sALN25xZXrst5+5dd/90jtOXbnHcuxHMuxHMuxHMuxfAb56QSWD29fBuzty23o\n3t37KbvqwuJpVlaEH2l3b5/Lly4BcOXqVSzrgJ37U1g1h69/piSHnn/qhQlPBQQ/j3zlF7+AYylL\nuWmBBo82GnXG9DYwOVWgF8mNrzI6jtmVm+rCwhkqeXFj+b5DvqjZTXHC2obcbErlUSJFVMrFWaYU\nxj53bpFuW6zs7373TZY+lNvV9ExMqSbv9NwG5YJ8N18MOHlRoIj37/yQ9qB7pP7ZOgeJEafwvGEc\n9kgkqSvGNI2UwM0wTRKFqGrjE9Q1S8mtdzGHRHOZDJ5mOA00UL08VuCEBqTPzEwTxjKGr335tzj/\ngqBn+9st7j2Q29Xthxs8eiLraqQ2yomFU/JMs5NmmTxLpqcnmT8hiFe72SHQYPeIkJERCcgdHR8n\nVkJQPzhASbe2t3jjje8D8O67HzLw5GZ46tRJfv03vgrAxfNnsDVg1PwxJK+ft0xOTuBFEvyczdk0\nlN+pZ9jEWUUMLJcV5TAjM8/MrLiF9zodEoUHfT8kiAQlnbZrafbmg40GixMvALA4ewJTkf9sPk99\nZRkAb6/OzLxkNw0GQZpxZBjGAT9OGBN25cvNVouwf7QMSwArCki8IWIW4GaVjbzXwtZMJ8eyQN0n\nsR8MuTMxTDggrbcZJszFQJy6aCGrvFVRDBoXj5WEOIqk5WyDODPk1gE/kL/dXpC66A2MpwJ5o/jA\ntfNpMj0q+qG5UafZ2dH31snntBJBpUacyHtPnizy4K9/KP33LCY1q+ztd+7jKknozEwF3xdd9Ojh\nOpWyrPWxiaq200p59bKZPL4j+6JSqRBosDJJTDEj77j7wbvs7ohO+u3/4veZXxRdnkQxyRA1eEYf\nq6Us2TENTciPUFVurU67Q2TIb47OjFGpCsp4fvEsprr2Qzdk9aGEU9SzRYzrgi6vLa/Q6ch3u8mA\n770ra9xKIl6/IrqyUholV5LfvXPrHk3VVwPXpRSrvh4dxVGPAoUynn5+YqZAMXe0bGeAnJNL2b9J\nIKMcenEUpckrtmWlbtUkTtJzNGMfEJrGUYhpDRnLbQo5eWa0UsJXAuhCIYtpyXx2u10C1bdkEwJT\n0VnLwFJMxfSTNHwmcRwyujYz/S6rHwjqzK//6pH7+pOQT8qwA0neAtjc2uL+fUEOl+7dTbmtJibG\nObUo54FtOdSU+LbdbrO9vQ3A9vYysS9nST5jYVtH814M5adCcfDBkvgh33znXVaVhM4beFiafdY/\nVE6jUAh5vPZ3AKxtt/nKL34JgHzWJjq06IYp6IZhEKmC+xE1NGQ+OFzE43Dw06FnOMTwnhgH5WCO\nInut+5TKkrGRy0b4rqS93nuSMNVX1theRGgL/P7lr/53KRlmMOhjBMP07wqBuiICbAYr4iJ6462P\nyDvDw9vngqbh+kme770jGSp+lGVjSzb9g+V7TJ2U9N9eb5+5GdmII1W4syMxBXtOA2s+f6T+Odaw\nXEZ8KGU/SWPXMo5DZnhA2cahIT0Y02yxQG1EoNRms0tsKmGoEadKv6eZNJdevcT5M+rSDTw6TU3t\nbQ9wMhJjNX1yHBx5/slak9g3tL99bGVbHBsdIUmOtsSLhfxBFtJ+Mw3mCCI/deGFIXz/LTmUZuYm\nUyj55vsf8vjxEwAGfkioc3j/4TJ//Kd/DoD127/B+TOLMi72P0x81Nz8HI+fyBrJZKBSkrHZWV1j\n46G4zLJxmQ+3JC188aUiBUf2Zd5JyCAHr+846fz5gw4TFYmtyrQSsqasqfr+LtlYDrZGo0FH6RHG\nJifS9GzPd1MG6iSOUyPKsTMYqscG3T6hdzQDA8AkwutLHGIukyefGVJvQKEwjO0gzTRMkgRDX28Y\nSUoTksnYhGrw9D0/zVCy7Ey6HywjAqXtMJIQlJYha4GlmaaJEWPpwR8G+TTmMYmfvrQdscIEM2Ny\nGHYmKoxNyHrN7ZrsaRzUxmgeT2N8iuVJJsZk/ra3G7z0spBjtho9FuYlbMA0fGztTz6XZUXXR7cr\ne+7kwgiOkjP2ut2UsLNUOpTJaJtY2v7HKxv01P337W9Y/PJv/j4AcyfPp0ap8QwHyMM7y3gtjRNt\nutxaFFqH//5/+G94+6bEhP3wB+/zK78k1QPCekCnrin/AzfNhiyVKizOi849OT1Ha1/dwmbCb35R\nxmJ8fIrpRdGnD9Z36NaVHHi/kbrGBt0+oWbATdVGyCkr/9zYONaCvD8a9Xj1evNT+3VYojCkq/pu\n0HcPwIL4IGO8XCmnFQYMDAKNi0qCSA0p2G23iFQPOXaGvGa8R2FEXuMWdxvNNOaq2w+Bgf5uSEFj\nD7uP5+V4AAAgAElEQVStFj1lMk/cgL6Gm2RzNnlf3dEbK/Q27x+5j59F0vjpOD44Pw5Rxfi+T70u\n7dzY2EqzTFdXl9OxGh8ZZVIpCwr5AntK+Ly/t8ejR6Lb+v1+Sqlj2ybXLwuFxUilxNVrN56rzcfu\nvGM5lmM5lmM5lmM5ls8gnzsSFZNw87a4YB6tr9FT4q5Ws00Ui7VrWQ7xsCCoabN0V2C5e0uPGfTE\nbfALP/caU0oE53v+U2jHkD8jMQxSwswfBxofvvEZh2DCQx8f5sw4iuz1H9GPxVrPWXnqbXGBNMNL\nNNc0q27Fou+pOySZwO/Lzeb2+x9gqk9gtFTg1BnJdBqfX8TMCfRoFLK4sfJzmBlW9+Sm13DvY1Xk\nRnnu3DidlljZvf4TTFugSqcQ4Rpye9zouGy/JbexTtckM3q0Wk+OWuwhYRq8J1dpGSPbMajVtGRK\nAoHe4M1D5Vss26EyInws1spaCgZGsYHtCAJx/aqgZxfPn0r5dAIvTOs4hWGI4Q6RsIixMen7V776\nZco1cXP88L1bFDS7olyp4HpHu+HbhkHW0RpdUURRUamJyQly+r7BwOf992Ut33twP0126HZ6RIqm\nJYaVriXLclhfl1vQn3/z25R/57cAOHli5tCt/Kfn2oujCEezQi0roWQKYrPy4fv0dobZWifpKkqz\nu7JKsCXPeOsG5UWdY7tM4ksiSDjwmZqRYNwR36att8QPb/+QgiNzMjoywcKi3NgLlQL7GugZJiGh\nrv04PkA5TcM6QG/CgBGt2XcUicKAfl8QXMc0yahbzbJi4shL34+hQbQR9FuyP1x/QFmTC0qVPJYm\nUTTaXVZWZN8kic3klCBvuUKBYUWaJOvg94doSC8tCVStlilmddzCHgPN7ozipxMKPP9oWU/DQOBq\nMcvClLi87axFvbmr7a6l5ZNGaiVeeUX0SRhmKCry+MrL53jhorj2/uqv/wqvLuN1+uwMhaJ0aFx1\nref1qY1o0fFshv19md9ut5tyuZUKefKaMFQpZGh15Znl+zf546/JWvqd3/2XTM4MSVOzn9rH0+cX\nyWox8kunT3HxlKDSpxYWmJ4U19uVs6eZKsq6+P7fvkVTM+aarQavv/YaANdefAnXC/XzFsa+rDvT\nC7ikRJ3zV67zYFf6/2RlnbyGZcTEBN4QQQxw+jKfk0mWKzWZfx+D87Myjls1n0e3lj61X4elWCqk\nKGyv1yP0hzxkYVr8OQgiBgMNGi8WU061KHDJK9KJCQUtCt9p9xgmz/lxRF+RUSPy6CkXWKvZSQlu\n+7aBo2jY3oMHJJ78na/l6fS0IPlgQKIu95IZkw0PF2j6ychhQuLD5+7Q1d/vD9jYkID2lZUV9nUe\ngyBM0eVi2SGjSVyWGXHrI0EvPTfAUtuiO+gzNSNnxvj4OAVNOrl05ToZ1f13Pnyf3GMJeZiYOXWk\n9v9UjChDs7XcyCevB7ddLtLXuIdWo8W+Zp2YJoyOymG7tbHJn/zJn8l7ooSXtfL2mVOLQ75JoihM\nfcUmhwJ1fow/7kdZzA8RdepXTMN6LqZduxATJQIfNhonuHlPFmOSbWCrzzmOTPb3lcncDdnbkola\nfrhF0JfMFr+9T/kHwjT+6uu/zKmzAjmXR/PcvS9uJCM2MJVCYGdnj6Ar7zSjOhevyNjWRhfxFI4N\nkhBflV3XC4jVnTBWzmBrjalnyXCssUxidZrGiYFhDuFQI42biuIIc3gYmoaMJWCYFllNK3dsh0iN\nyDhyuXRZFuurr0g8TRS6eJoWPgi8tGhruVQhq/Wy+n2XZkuLuXp9FhbkQFm6t0ykdfymp6dpqivw\nKHLutLTj3tLjlK16eno6zU7ca9VpNls6FtU0JiRJjGGIDYEfMFw8tpVJv/v48Srff1OKTo+M/DIl\nza75NMK4n7R4hzIVTRv2tiUOcePJHolC9mOVPGPj6r7sNjE60rH2WpvzGquYWA2CjvQr75QYycm4\nhSUDvyVKf2J8lJlJuUzcePk1Hq8I7L706C5BMmQp9+l2D+Lyhi7hAD81nO2ixSs/e3R43fMGBNqX\nKApAi1tbFti2zkvGSLPPWo1mWrx6Z2+H85fEtTMzP51m57mux+PHyzIOTZczZzRV/uwi8ZCI1gBL\n3bRB4NNtyzqxjYi5WSV/HeSox2KU9r3kwJD+2OHxaXLqnOyRsNshDsX46zf3OH1KQghGJ6YJgn3t\nW4u5OTk0nqztkNN4mUsvnEhdH1/5yi+xruzkE5NlaqqfDSXMfO+9bdrqwju9uMi4knDmcll6WoFh\nMOhRKcm+zGRzBEoYnIQDNpfF/faNP/q3/Npv/7c6tpc+tY+RH+HbMsaZMYeixpV2oja5aTn4FvoT\n/LWeDW+8e5NCQdo9Uc2R0/Og2Whhapby2OQ4WY37CzwvpWHZ7fRYuiOZvb12k0xF9mXGcQi1CLY3\n8Oh0ZN7WHj9k7BW5EKz06pjqOjwzMsn7K61P7ddhMU3IKPnr2HgtNZw67U5KjOlkLPJ56VcURRRU\nf2IUSTTcIG9ZZHXfjBezZIdxTbFPqDGvQc9npSVrIpMrkMlptYGsT68jxvdy/R6OhmdU8qPU1KhY\neOESs6dkf9eqZe6+++aR+/hxGe45oVAYurKTp9Z+pyMGbavVYntbLi5bW1upMdlutcnmtMC8bZNT\n2o69/Ta7u+qKbg2wtVpCtTLK3KwY4cVqmYISjcZxjKuEyrZjc1ONrpvv/ZAkI2vgtde+dKR+Hbvz\njuVYjuVYjuVYjuVYPoN87kiU7VjMzgn86b7/g7TmlmHZ5GpiWWcKOfpK279b32O8IkjU2XPnJMgX\nePf9uzx8LDfnX/j5n+Wl68JTkcvYhIphWoeyFhLDSOvNHZb445/9qDcPw7BIjpgtA0KN7wcCqX74\nAWzsaBmJkS6mMdCfSRi0pY9Jv09vR1xvhrtL1BWoctCqYyh529q9d8lq9tfJa18EdcOsPP6ARLMu\nyjmLnC2/+2DpLV784s8AMHWihu/J7cSPe3iJjE8tKpOoe8uIDEzraESUwzBow4BEYakIIy2RZZqk\nELHruViO3uYyuUMkkxb5kmb+jI2w2pRMwpmTo3zxhvA9DZRLqBsEmHoT6g76aUag5Tj03GFtKOj2\nBXLe3tmiq5XMbTPhzh25/V67foOM8+mug8MyNyOugsD3qGutKcd2CBTV29reOkTnZhBrokMcS+1C\nEO6uYUCkkSQMy6cnBrz3vtx2FhZO8IVXhFzWNJ63Lvpnl+pIlVxRbpV7O2s8vCsldNr9hNqkIAne\neI+21icbK5VYfEmQmXX2GC/K+JQrDpEvbsrxyhnytqAdpYkMGVPWe79bJ9AyR09WV1hRTrh2uw36\n/larlbr3S6UiTnaYnJDBV5diu9Xijb9748h9dPsukWbsHHaYObZFXm+wpm0TKDFPr9dKOXTiOGKY\nqtcdhPTVnZOYORY0+NgfhJSVpM/tDxgSpEWxkSJXpmWniLjvuQSu9LFayOGq/uu7/bTckWEYB5lu\nz5DZ04LitLfWyOseqbc7DNT1sb1TT0kCl+8+ot8VlKnrh3zta4LcjFcz7OxJn7/85Z/n+nXJSjVM\nj4uXLgOwdFd0bbm4K3MGLC6eI4pEb8VRSDMQ5KVWK9JURPGF+fnUTdXvdSgUZV/ceu+75PKCcP7e\nv3oGEhV5FLVvK1sruMrBduFLp3lyU9CG5l885OZ7QgzabvoMtDzKfNmivipJHhsrK+QUuS7WRhgm\n6g76Azrq/ttptNjYl/4NgogMoqPMBHw9VwwSOqif7HSBj5Y1Ueruff7pr0ppmIlChRevn/nUfh2W\ntdXVlOgynyukjpOx0Sqe1huNkjgNVclY2XR/JLGR1kw1/D7RroRuRL0We/p3Y2eTaKgr/Qhfs/+m\nLl5FoycIt3cZ0Wy13/z9/5r1B6IPMq7HpGZHhobB3qqM+ejIGJduvHrkPgJpsohhSJKFdMAiGe4/\nv5sims16h0310PT7nbQGq23ExFpqyTIiXNX7O7ubaaZhEkUQaSb4yBTzmgG8sLDI1JRyL7oD3n77\nHQB2d3dTzqh2Z8DJk4LkvvC755mZnX2uPn7uRlQYhZw7I/BnpVqhoUrTDVx8dQ1ZiUVpROInqpUq\nnsLH/sBlZFwGIJPN8XhVNrb7zW+nWQXXLl8SpQU85Ro57M5Lnk6RfJanLgrjNEvnKNIfdFlflrb9\n8O1VRielv6VSFiMteOhRUGK+TNFjdSAw6v7GI3K2bNCiA/iimDaWb+JrYdfALnD9S0Iyd+n0CdpN\n3TRuRKgbxc316Poyhq1uiYxmpZmGScbsaUsDTCVaC4LwKcbcT5MhYabxVMyZmfbNNBN8hb73Gk0K\nmiZdKdew9FAyDRNLFdr84ml67YN6dOsb4urptuRwsg2DihJmWo6FqdZat9NKGZMHboSrh1On00kz\nfmw7pqckpasrq6mb8dkSU9Q1dfHCBW4tiUKp7zXoqPHb6/UZ0dgrA9IMmTiGcBjTEocamwdxaII5\nTM9PaKvi/u4b32fhpGzU2enJNHf08zanRkdrtNvS5o3ODmFD1kUhk8NW92J2ajJNb4+6fe7uiPEz\niH0u5MXwy9fm6O6J+31m5AbFosTPZII280ou+vDxbfbqssZd36Cv9BUZJ0OsLrasUyAqaoZn3qZY\n0RqJZHGVusPwwN89esrxoO+S6FwkMWnmmWM5ZNSFE0RJqh/K5TxXrgnDtpMvY+Vk7d6+t8Z+U8bH\nNGyqZZn3ifESGc2U9QKP3kCUe7PjpsWII0wsW4sUhwH1fXGlzEyMpK6XJOmlMS4A/hGNqKnzPwtA\nvwu7y2KU5/MreEroWcmX0mLgGA5LD+VQqncHbKghOztRxdT98s1vvUGjJfulOlrgxnUxFr/8s5L5\nNlKd4uv/8S8AmJ4aY2VZLj9zJxcpV8Q1srO3T74ka6beauMOs6jNHOViWcehw87moyP18erleebO\nyCF49toil6bFOHnU2OOb/5e4xDv3HqVs99OjZeq+kkMGPpGvMU5P1vDUDZQtj2FrhYXA89O12Wp3\nSQwlSXVyFDUmqlYukRmeARmb6QkxNv7x71zh5gfLAFzMR/z2VVn7tfGIK//qtSP1DyCbzWPltLJD\nNpcWjU7ihL66iDO2hXpgyZtgKXWKu79Na1Xa4NbruEp02mvuYyqNSml+gZHTEveVLVYoj8sFqO9D\nX40Wo1ihpX+PniyzeFn29/L3vsX+9r62x8HriQ57knM4ff3ikfsIT9MReO4w27FJR+Mid+ttEr1o\nt1sNuhqe4vkuXc3yzGdz7ClR887ODlmdx3KlTD+WMbGcOI1/y2bz6f7zg5AHD0WXJ0nCwoKuq7Nn\nWV8X8OLy5Rd4SUOFLMt6boqjY3fesRzLsRzLsRzLsRzLZ5DPn2wziRnXrKyLp0/z/Q/eByTrZugO\nMowEN5CbYdbMYGT19uhk2ddAM9t1GZ+R23uztc+//4P/AIg75Mrli+k7h6iJvPfAokxLbnDAOZH8\nmODzJEmIg6MT/AW+T31bEaQn+0RKXuf1e2mwrGGaB7Xm8lUWL/wjACZnz1LKyzN5y6DXFwt9ZWMN\nP5HPl+5v0A/kBvbLX32Nl1+SjJuH9+5w+wMJOK8WJ+nsSJu3o5AxhWOL5VGiUG5dZtLAcLQidzA4\ncuz8YT6bgxpxxgF5nkmKBnZ6Hm29/c1PnUjRGsyDsg9Ovoir2Wwf3n1ISwOBT2rmRLVQTGvU1SbG\nUdolmo193J5md3Z6RJopYgINLWlRKhaG3IDU9+qUypNH7SUZdff8ws+9xsKCuDi+/e2/o6muj8D1\n02yyJAFHOVniKMZxgvQ9Q7dOHCcHpUbMCFvRwdW1nbRMzeTECLZ1MKaH2/MTDzj3Qva2ZJxae720\n3EnkHNRKG5+cYnxSUJcdf4XttqCeVuSkKFwcB8yNijt9buI6BVMQie7OJjvb4koJAj8NuvYDl6FX\nt5grpFldjpPFcYbusAEZ3fdxZOIqwkOcYDxHlofnBhiKikZBQhQOSQmTp3TAkCNmbGIEJ6tEh2ae\nx2uyj7/17bd4siouy0zG4fILgoZcPHeSkWEYQiZLJid99xp96hqS4HtBGjTu912CntbPHK+me8A0\nzadu6Ueu+2mKC2Lm3JeZWJA2FcZH+Ks/E1fdWKnAfqOrfRvn/or04fS5BU4uyP66f/sueeVSK5VN\n9tuyp+LY5OEDQYtGtCTGxPQcX/ryK/L35AgXzi8CsLaxg686stEckM3LOLz/wQ84f06eefjgCU9U\n583PzeK77SN1cTtsEe4JSjARl/EnRe/fe+MhSx8pWWzPparDd/LkFHveMNOtw/0nsgbr23spUWhi\nF9OA926nTV0R7W67S155sIJ8iJnIWZWzbarqUhzkMmz7otOa2S1uvCx9PfPiRXzkPaXsVXKzhSP1\nD6Qu5TDhZ+AOiBRFIQopKHqdtBu429LfxuoyXc1Q63T2MLPDOpZj5AuCAk5fe4Wy8iNVR6u01J0+\ndu4C41OLANx58w6u1nNN4pj2Y3FNfvTkNhcuCRLV3dlkQ2sxZqwM+bKcJa2oS64maP34lZ85Uj+3\ntiQ4fDAY0FfusW7rCZYpa3R3D7qacV2tFXA1Q3Bre51I9/F616U1REtrVU6dlnXf73r4qiaymRhD\nPToPHj+ioS7g3d1xXtNszbNnz6alXjKZDG++KUHyuVwGR4P8Jaj/gAT3KPK5G1EmkNFG/cxLL7P0\nSKC1bBTQ0iKYfhwcUBxgpAVBM3aW8oQsaq/v0lM4s1Ap46lb8N/9f1/D4HcAuHr5hYOCr4aRHv4J\nyQENwiGV/GMJ0C0zJfM8ihiJw8yEZAC4nds81JRpJ1PBVmOgUq6QK2tBw3yVSkUWfrF0nvK4HPRT\n42OUKnIwn3c9Ol3pywfvvUkYqhtmI6DREUNrfaXN3o4snMDP0lPl2dyKiS6KEXB15gKFgi7Y/dv4\nsXzXtiAxjpa5NjRiEoOUIsLASAkpTdMk1tifZqNFW2kprr1wjbwWTrZNIy0qvLmxwdqWKHfPSGg2\npX3RYBmA6alJ8ppFkS9XSbS4cKtdp63Kb2NrPz2QojBJSQCvXr3MyoYc/Nl8EdM+KthqkM8PFX6e\nkyfELbWzucOTRxITYFtWataEYUxO4XjDMNNsPsu20zgADFImasOI03i8wI+4pxlhN16+zIRmPInG\nP8ym/5M1onq7dVp1MaJMy8bMqxGcN7G1Hly/22ZpT1zTrf0dwqEicWL+6E//PQALJxc4eV5iQYrW\nFIOOKPd7927zN3/9DQBGxoosaPHbII7SwqimaaUEqHFMSrLb7g7oahq275KyOSfJQRbNUSSJgjRe\nxHe9tNab7wccBPEZaTyKYZoYasT6YUhf9crm1harWkWhWMgzMSLrcaSaIYpEEZdLFSo1ObRqlSrN\npny36/WJNEYwHHi4Wnux2+2j4YwYJGntNIDIeg6Fg7hoclUZxys/+1tUKqJb/vwP/l8Ko2IsXbp6\nGW94kE7PMaYu8vGKQ6sp7pps1uJLX5CYxCSOyWXF/Xb3rhhTNz9a5l/8y98D4K03vs+Zs6JXGv02\ngTGss5fhnXfkUnDu3CyeGgS9fh9bn9ne2aMXHk3fmFMmXk7dOnaX+/tyZty+dY+uulj9wKeo/BKl\nQp5TStnwnTd/wM3bYkTlLKiUZd6Cfpe+xk31BwP29D2RH5GyfxuuFJcDwn6XqoZfdHyf9T05e27u\ntbiocW3Wz53m1kD2U/3dv+HsKbnMj356yJe0odNOM0ftKMDUmLLmygptNQIDr4ertSWDcMDUtOiJ\nMy9fY/qEuF3r62tsLAn7erZbp7UjoRHLrQ59jSm6nHXw9TLRDlr4qveNTpdIzyrbMdi4I0Sa/Z06\nWdQl7Dh46Jjs77NyU+b52m88u4+7u7u0WkMCUoORUQ3XsBzu3xf912rnMC25GDSaPltKGF2v7+Nr\nbKPnhti2tOfGjS+m719a+gHD20q5PMLG5oq+38YZ1o7N5VKW8snJyafoFG68LFm/7e4+/b6st2y2\n+FxnPxy7847lWI7lWI7lWI7lWD6TfP7ZeQnEalGem1/gNa2Q/MYP3sWzh0HXEb5S2ocxmKbc6ntR\nQFXRgbHpGr6Wjoj8gNKwArOd4c++8U0AZsbHmJwUVCeK4zR41EgOqPQN03ymO880bJ4sC5x5dr7y\nzD4OBhEf3ZRyKsQdKiXNqMgn7Gh9nkY9SLlJTDOPYSpPUJKlUpUbRm1snEJZXQX5KpZa6DEQa3bF\no9U+VlbdFcxQVreTGQ/otwXdaTa32W7KjfJissDcvLy/WJrizpJkOmUdi9jYf2bfgDTwOTYOWd1G\n8jFKLvmHhUWzrmSg7TY1vSEnUciuQrsbG2vEscz3ibnZNDi+O1wDkyGtjtzwrK0epqXZKl6brSGF\nfz0kqxDs7naDYlFu2bs7+3S66j4ZmT1U8OfZMnQFJwkHtZcmx8kXtIQHpfSG4/te6hJyHJucui2y\n2WzKfSSlC4boB2nZIicLW9vKz/JkjYnxCf1d43Ml3uy2dilXpf1BVKTfkltusVrAVyLKjbUVTCXp\nixOPmt5+C8UiH35dApkzZJiZ0/p0/YBeW26bvjegqFw2U5NTlCuyBpvtNlGipJ0DyDiCDpiGSaLJ\nJa7bR8EqGs0eUTSsVWem/D5HkYxFmpUbBn6aNYphpfxqVkJKfpbE4GvQe2fgE4UyJqdPTpC4ckvP\nWg6G8irtbKwReLLWwjGXrL6zlCtS1nUy6Jr0Blp7MfDTAnvewCOM1YUaRSlKma6R55DYMImjnHZt\ngqk5Qf0unJpio6Ukhe6AL70m+vZrf/xtHHUT1UpZqieEw2v58WNy5rC234CGZhNPTAsSe/veMn/4\ntWGF+we0eoq2TOR5oF6F3d0WExo+0Ot6Kf/RzOwEV9VF5BgGHbdzpL6dPFFifELGNZsLaWzJ9zYf\n79Af8opZER0dtp1Oj4KGg7TchJZy0LUS6GsBRyvqpUkAcRClZXywDKJoWGokwVCosN92sZRAea3e\np6/vtMIMribFlLcjFubFrb3SfYj/QFCUIwBRmH5A3FUUa+UBrScSsN/f35GsXqAyOcWUZoXOXLxE\nQT0WHc9nVIlWu15Esi7vGUQx1QmZh9LlSSz1fOzs9vib7/5HAGoTs9T3RO8XDZeXf1UIgMdm5mnX\nZR/3koD2pvSlESZ0VVfVEpuRkaNnrmWzWRa1bl0URbQ0C7ZQGmdsXNZrvb3D8FQJvZhqVdqfz+XZ\n3pXfLRRsmurOe7K6xsUXBDltduq4PdnfZ84sMnBlfe/vN3jhkszLa6+9lrrw+v1+6qlynAwZLQnV\n6bboD7QW6NQJPHUvDnX9s+Sn4s5Lk9xjg698SbI+Hj58yHfe+FsALl1/kaKmDe81O2ANKZ9t+gpn\nxpFLRjO9Mo7FYEiUlbcZK8kgvf3227z+umSxDes6yXfjQ4e/kSbuJQfJZmIDpGTcIW+/9RYAr3/x\nwjP7OOi1uXlTnjetDC++eAUA1/UxNR5p4JlkclqrLnEIwyEjc5hSFuxuPcbakynJ5qp0OsrUHZtY\nGT14MiPky5JpsXDqCiMTsqirlSLj87LhyEym6dm3725jIWOVtQIyhsRT5cpl6kop8CyJDxmdQ5K3\np2yTJMbU+JqZmTlWtJ7R7s4uZ3UTNXb3WV8T//729gavvHRFx67Hw2WN2coMidPWKRelze3Ix9G4\ng+XVde48kMUeWdMHBkdkc+KkjMnq2gZlZTLHtHkebPYg9uvgs9nZKUa1XlmcRFhq+IdRmGYHJkk2\nNaKq1UpKDDcYDFI3lhU/bbz3tb7b6toWr7yctoDnYnl9TjEdn/nTonyNbRd7V9pfG6+ljPPd7kHB\n38RyyKvRNV6tcva8ZJ2em79AEqr7YX+NgcYtzk7PEoRyhHheh3ZL5i2IvDQeqdfzSfKaumzbeOqi\nN00jdbRnsxk6bXXhmTEYR2dJzmQcIp3zMApT1uM4jp6K7RsWPw+CME3h39jc5ckTufTkwoALyo5t\nJhadgRhU9c09fI3hs4yIvMYzVkamUzLLbM5g0B+S0vpY6o42bINEixFHiU2kB3mEXB6fR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nd3sNx4jslCsV5pScM04NqfZgvHn7bZKx/Lz8cIlF9YwNxi3ubtwCYOFcg7NP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3ZwPg13vddHyArMvCxwcUG05fn5OVLeTFLKJVfdQhhY5yBkXflV+PT/IAgQ8PWTpIWwdqONp14Z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\nHEQxDMMwDMOcAQ6iGIZhGIZhzsD/AnlPYZtKPeV5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f928a5fb610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print 'Train data shape: ', X_train.shape\n",
    "print 'Train labels shape: ', y_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Validation labels shape: ', y_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'Test labels shape: ', y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print 'Training data shape: ', X_train.shape\n",
    "print 'Validation data shape: ', X_val.shape\n",
    "print 'Test data shape: ', X_test.shape\n",
    "print 'dev data shape: ', X_dev.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 130.64189796  135.98173469  132.47391837  130.05569388  135.34804082\n",
      "  131.75402041  130.96055102  136.14328571  132.47636735  131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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tzI/nwTbNbAJOfLMKOfHNKuTEN6vQ2MSX9AFJj0t6StKzkv6tWX6GpB2SXpT0gKfJNnv/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\nE9+sQv8PhW6BNuofxXAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f928ab95050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print mean_image[:10] # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print X_train.shape, X_val.shape, X_test.shape, X_dev.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.056040\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "print 'loss: %f' % (loss, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: -21.030787 analytic: -21.030787, relative error: 6.343291e-13\n",
      "numerical: -55.911471 analytic: -55.911471, relative error: 4.348354e-12\n",
      "numerical: -23.563209 analytic: -23.563209, relative error: 3.209687e-11\n",
      "numerical: -14.022300 analytic: -14.022300, relative error: 6.945531e-12\n",
      "numerical: -5.166736 analytic: -5.166736, relative error: 3.778776e-11\n",
      "numerical: 4.190686 analytic: 4.190686, relative error: 7.748555e-11\n",
      "numerical: -13.460558 analytic: -13.460558, relative error: 3.215904e-11\n",
      "numerical: -25.034816 analytic: -25.034816, relative error: 1.097007e-11\n",
      "numerical: 8.879367 analytic: 8.879367, relative error: 1.403402e-11\n",
      "numerical: 16.101044 analytic: 16.101044, relative error: 2.348291e-11\n",
      "numerical: -25.579323 analytic: -25.579323, relative error: 5.518102e-13\n",
      "numerical: -7.857061 analytic: -7.857061, relative error: 7.759999e-12\n",
      "numerical: 0.457842 analytic: 0.457842, relative error: 9.200583e-10\n",
      "numerical: -18.783472 analytic: -18.783472, relative error: 9.624315e-12\n",
      "numerical: -9.040995 analytic: -9.040995, relative error: 4.679635e-11\n",
      "numerical: -28.495001 analytic: -28.495001, relative error: 1.705602e-12\n",
      "numerical: 4.619240 analytic: 4.619240, relative error: 3.027572e-11\n",
      "numerical: -5.114085 analytic: -5.114085, relative error: 2.982017e-11\n",
      "numerical: 3.527877 analytic: 3.527877, relative error: 1.056164e-10\n",
      "numerical: -7.172330 analytic: -7.172330, relative error: 2.331349e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 1e2)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 1e2)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *In zero point, the Hinge Loss is not differentiable, so gradient check may fail.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.056040e+00 computed in 0.215242s\n",
      "Vectorized loss: 9.056040e+00 computed in 0.026311s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Naive loss: %e computed in %fs' % (loss_naive, toc - tic)\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print 'difference: %f' % (loss_naive - loss_vectorized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.191279s\n",
      "Vectorized loss and gradient: computed in 0.009903s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Naive loss and gradient: computed in %fs' % (toc - tic)\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.00001)\n",
    "toc = time.time()\n",
    "print 'Vectorized loss and gradient: computed in %fs' % (toc - tic)\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print 'difference: %f' % difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 781.254495\n",
      "iteration 100 / 1500: loss 286.353236\n",
      "iteration 200 / 1500: loss 107.755959\n",
      "iteration 300 / 1500: loss 42.610920\n",
      "iteration 400 / 1500: loss 19.201076\n",
      "iteration 500 / 1500: loss 10.262567\n",
      "iteration 600 / 1500: loss 7.499009\n",
      "iteration 700 / 1500: loss 6.211038\n",
      "iteration 800 / 1500: loss 5.644527\n",
      "iteration 900 / 1500: loss 5.112182\n",
      "iteration 1000 / 1500: loss 5.610113\n",
      "iteration 1100 / 1500: loss 5.113341\n",
      "iteration 1200 / 1500: loss 5.106407\n",
      "iteration 1300 / 1500: loss 5.440010\n",
      "iteration 1400 / 1500: loss 4.820498\n",
      "That took 12.088552s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print 'That took %fs' % (toc - tic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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BW4FLitUuBm4pbt8KXBgRrRExHpgEPFjLmg48EF58sZZblCRJ6j71HjYdAdwcEanY17dS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\nDHPmvHH7o0bl4DhyZN7e/vv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Y9osAEfEoMA7YUXi7qfj9MDC2E89nQ0ppW/h8HFifUtoa\nEY9v9/gfpZRWF/u/ETiJfP3iY8hhLoB9yeGS4m83IanpGd4klS2AT6WUfvSGhRGnAOu2u38acFxK\naUNE3EsON9u20dl9bbOhw+0t7PzzcMMO1tnMGw872bfD7U0dbm/d9viUUoqIjvvo2HMXHe7/Z0rp\nr3dQx2vJi1FLwmPeJHWfbcFpDTCgw/I7gU9sCzYRcUhE9N3B4wcBq4rgdihvPO5r43bBaNu+fgZ8\noDiubhjwDnbdU9fZ57AYODKyg4Bjd7DOrh4PcEZE7B8R+wHnAfcB9wDnF7USEYOL7e9uu5KaiD1v\nkrrLtl6j2cDW4sD7/0wpfTkixgGziqHCFeQws707gD+MiDnAfOD+Dn+7GpgdEQ+nlH5/275SSjdH\nxPHAY+ResL8ohk+n7qS2ndX8hvsppfsiYjH5RIp55CHV3W1r+789SB4GHQ18M6U0CyAiPgfcVZxR\nuhH4JLBkN9uV1ETCXnhJkqTqcNhUkiSpQgxvkiRJFWJ4kyRJqhDDmyRJUoUY3iRJkirE8CZJklQh\nhjdJkqQK+f9Ci65LNphaLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f928adee3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.373163\n",
      "validation accuracy: 0.378000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print 'training accuracy: %f' % (np.mean(y_train == y_train_pred), )\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print 'validation accuracy: %f' % (np.mean(y_val == y_val_pred), )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 788.382734\n",
      "iteration 100 / 1500: loss 287.401584\n",
      "iteration 200 / 1500: loss 107.902239\n",
      "iteration 300 / 1500: loss 42.305258\n",
      "iteration 400 / 1500: loss 19.204674\n",
      "iteration 500 / 1500: loss 10.245005\n",
      "iteration 600 / 1500: loss 7.327785\n",
      "iteration 700 / 1500: loss 6.323890\n",
      "iteration 800 / 1500: loss 6.214511\n",
      "iteration 900 / 1500: loss 5.760209\n",
      "iteration 1000 / 1500: loss 5.415971\n",
      "iteration 1100 / 1500: loss 5.091169\n",
      "iteration 1200 / 1500: loss 5.171165\n",
      "iteration 1300 / 1500: loss 4.879406\n",
      "iteration 1400 / 1500: loss 5.027432\n",
      "iteration 0 / 1500: loss 1556.851452\n",
      "iteration 100 / 1500: loss 211.213674\n",
      "iteration 200 / 1500: loss 32.498450\n",
      "iteration 300 / 1500: loss 9.850085\n",
      "iteration 400 / 1500: loss 5.915143\n",
      "iteration 500 / 1500: loss 5.557434\n",
      "iteration 600 / 1500: loss 5.101645\n",
      "iteration 700 / 1500: loss 5.748387\n",
      "iteration 800 / 1500: loss 5.906949"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 5e-6]\n",
    "regularization_strengths = [5e4, 1e5]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "pass\n",
    "for lr in learning_rates:\n",
    "    for rs in regularization_strengths:\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, learning_rate=lr, reg=rs,\n",
    "                      num_iters=1500, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        train_acc = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        val_acc = np.mean(y_val == y_val_pred)\n",
    "        \n",
    "        results[(lr, rs)] = (train_acc, val_acc)\n",
    "        \n",
    "        if val_acc > best_val:\n",
    "            best_val = val_acc\n",
    "            best_svm = svm\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print 'lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy)\n",
    "    \n",
    "print 'best validation accuracy achieved during cross-validation: %f' % best_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print 'linear SVM on raw pixels final test set accuracy: %f' % test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in xrange(10):\n",
    "  plt.subplot(2, 5, i + 1)\n",
    "    \n",
    "  # Rescale the weights to be between 0 and 255\n",
    "  wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "  plt.imshow(wimg.astype('uint8'))\n",
    "  plt.axis('off')\n",
    "  plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *you can treat svm classifier as template matching*"
   ]
  }
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